电网技术2023，Vol.47Issue(12)：4937-4947,11.DOI:10.13335/j.1000-3673.pst.2023.1376
基于图卷积和双向长短期记忆网络的受端电力系统暂态电压稳定评估
Transient Voltage Stability Assessment of Receiving-end Power System Based on Graph Convolution and Bidirectional Long/Short-term Memory Networks
摘要
Abstract
In order to quickly and accurately evaluate the transient voltage stability after the fault of the receiving-end power system and locate the voltage instability nodes/regions,a transient voltage stability evaluation method of the receiving-end power system based on the graph convolution network(GCN)and the bidirectional long/short-term memory network(BiLSTM)is proposed.Firstly,based on the time series response characteristics and spatial distribution law of the transient voltage,the topological connection relationships of the power systems and the electrical measurement data of each of the nodes are considered,and the input characteristic matrix representing the operating state of power system is constructed to effectively adhere to the temporal and spatial evolution law of transient voltage.Then,a deep neural network combining the GCN and the BiLSTM is developed to extract the spatio-temporal features of transient voltage with the greatest correlation.Then the mapping relationship between the spatio-temporal features and the transient voltage stability states is established to realize the precise positioning of the transient voltage instability nodes/regions.Finally,the proposed method is analyzed and verified by the modified IEEE-39 node test system and an actual power grid system.The results validate the accuracy and effectiveness of the proposed transient voltage stability assessment method.关键词
受端电力系统/暂态电压稳定/图卷积网络/双向长短期记忆网络/电压失稳节点/区域Key words
receiving-end power system/transient voltage stability/graph convolutional network/bidirectional long/short-term memory network/voltage instability nodes/regions分类
信息技术与安全科学引用本文复制引用
姜涛,董雨,王长江,陈厚合,李国庆..基于图卷积和双向长短期记忆网络的受端电力系统暂态电压稳定评估[J].电网技术,2023,47(12):4937-4947,11.基金项目
国家自然科学基金项目(52377083) （52377083）
国家自然科学基金委-国家电网公司智能电网联合基金项目(U2066208) （U2066208）
东北电力大学博士科研启动基金资助项目(BSJXM-2022104).Project Supported by the National Natural Science Foundation of China(52377083) （BSJXM-2022104）
Joint Foundation of Smart Grid of the National Natural Science Foundation of China-State Grid Corporation of China(U2066208)and the Doctoral Scientific Research Start-up Foundation of Northeast Electric Power University(BSJXM-2022104). （U2066208）
